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Salient Region Detection Using Diffusion Process with Nonlocal Connections

1
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2526; https://doi.org/10.3390/app8122526
Received: 18 October 2018 / Revised: 18 November 2018 / Accepted: 22 November 2018 / Published: 6 December 2018
Diffusion-based salient region detection methods have gained great popularity. In most diffusion-based methods, the saliency values are ranked on 2-layer neighborhood graph by connecting each node to its neighboring nodes and the nodes sharing common boundaries with its neighboring nodes. However, only considering the local relevance between neighbors, the salient region may be heterogeneous and even wrongly suppressed, especially when the features of salient object are diverse. In order to address the issue, we present an effective saliency detection method using diffusing process on the graph with nonlocal connections. First, a saliency-biased Gaussian model is used to refine the saliency map based on the compactness cue, and then, the saliency information of compactness is diffused on 2-layer sparse graph with nonlocal connections. Second, we obtain the contrast of each superpixel by restricting the reference region to the background. Similarly, a saliency-biased Gaussian refinement model is generated and the saliency information based on the uniqueness cue is propagated on the 2-layer sparse graph. We linearly integrate the initial saliency maps based on the compactness and uniqueness cues due to the complementarity to each other. Finally, to obtain a highlighted and homogeneous saliency map, a single-layer updating and multi-layer integrating scheme is presented. Comprehensive experiments on four benchmark datasets demonstrate that the proposed method performs better in terms of various evaluation metrics. View Full-Text
Keywords: saliency detection; Gaussian model; diffusion process; nonlocal connections saliency detection; Gaussian model; diffusion process; nonlocal connections
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MDPI and ACS Style

Luo, H.; Han, G.; Liu, P.; Wu, Y. Salient Region Detection Using Diffusion Process with Nonlocal Connections. Appl. Sci. 2018, 8, 2526. https://doi.org/10.3390/app8122526

AMA Style

Luo H, Han G, Liu P, Wu Y. Salient Region Detection Using Diffusion Process with Nonlocal Connections. Applied Sciences. 2018; 8(12):2526. https://doi.org/10.3390/app8122526

Chicago/Turabian Style

Luo, Huiyuan, Guangliang Han, Peixun Liu, and Yanfeng Wu. 2018. "Salient Region Detection Using Diffusion Process with Nonlocal Connections" Applied Sciences 8, no. 12: 2526. https://doi.org/10.3390/app8122526

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